Deep Learning Example using NVIDIA DIGITS 3 on EC2

In my previous post I provided step by step instructions on how to install NVIDIA DIGITS 3 on Amazon EC2. In this post, we are going to use an Amazon Machine Image (AMI) that I have configured for readers of this blog. This AMI comes preloaded with DIGITS 3 and the 17 flowers dataset from Oxford Visual Geometry Group. We will use this AMI to launch an instance on Amazon EC2 quickly and try a couple of Deep Learning experiments.

In the video below we show how to launch an instance on Amazon EC2 using the AMI I have shared. We explore basic usage of DIGITS 3 starting with data preparation, database exploration, training a neural network, improving performance, and testing the learned neural network on a new image.

Amazon Machine Image ( AMI ) for NVIDIA DIGITS 3

The AMI I have shared ( id : ami-5bac4e3b, region : US West ( Oregon ) ) has NVIDIA DIGITS 3 preinstalled. I have also included the 17 Flowers dataset from Oxford’s Visual Geometry Group in the AMI at /home/ubuntu/data/17flowers. In addition AlexNet weights are included for pretraining at /home/ubuntu/models

Image classification results on 17 Flowers dataset using AlexNet

To demo DIGITS 3 we trained AlexNet with default training parameters on the 17 flowers dataset. After about 4 minutes of training, AlexNet produced an accuracy of 67%.

By simply using pre-trained AlexNet weights and making some minor modifications, we see a huge improvement in accuracy ( > 90 % ).

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I was able to follow the earlier tutorial also to get started with AWS. I tried it before but it was too complicated. Thanks for simplifying it.

I got different training times than you. Any clue on that?

Also, I tried downloading the trained neural net with pretrained alex net weights but I was not expecting it to be more than some KBs. Its 165 MB downloaded so far and I am thinking of stopping downloading it.

Do you know how big trained CNN are?

Can I do an offline training on my CPU i7-3740QM @ 2.70GHz 2.70GHz 16 GB RAM? How long will that take to train a CNN to detect say just 1 class?

Hi, I understand that the original 17flowers dataset consist of over a thousand images of flowers but separating them manually is tedious and time-consuming. How did you categorize the 17flowers images and prepare them for NVIDIA DIGITS?

I have a related question about NVIDIA. I am looking to more information about hardware performance with NVIDA Cards for Computing Vision. For example: GTX vs Quadro vs Tesla, or GTX SLI vs GTX. How to use? There are many information and reviews about game applications, no to OpenCV/Computing Vision. Can I assume that if it is good for game it is good for OpenCV/Computing Vision? Thanks!

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I am an entrepreneur with a love for Computer Vision and Machine Learning with a dozen years of experience (and a Ph.D.) in the field.

In 2007, right after finishing my Ph.D., I co-founded TAAZ Inc. with my advisor Dr. David Kriegman and Kevin Barnes. The scalability, and robustness of our computer vision and machine learning algorithms have been put to rigorous test by more than 100M users who have tried our products. Read More…